I’ve been building GraphQL APIs for years, and I keep seeing the same performance bottlenecks pop up. Slow queries, N+1 problems, and poor caching strategies can cripple even the best-designed APIs. That’s why I decided to share my approach using Fastify, Mercurius, and Redis. If you’re tired of sluggish responses and want to build something that scales, stick with me.

Why choose Fastify and Mercurius over more popular options? Simple: speed and efficiency. Fastify is one of the fastest Node.js frameworks available, and Mercurius adds minimal overhead to your GraphQL operations. Together, they handle schema caching and validation out of the box. Have you ever watched your server’s memory usage climb during peak loads? This stack helps prevent that.

Let’s start with the basics. Setting up the project is straightforward. I begin by creating a new directory and installing the core dependencies. Here’s the command I use:

npm install fastify @mercuriusjs/mercurius prisma @prisma/client ioredis

Next, I configure TypeScript for better type safety. This setup ensures my code is robust and easier to maintain. Did you know that proper type checking can catch errors before they reach production?

The project structure is key to keeping things organized. I separate concerns into clear folders: config for settings, graphql for schemas and resolvers, services for business logic, and plugins for reusable components. This makes the codebase scalable and easy to navigate.

Now, let’s talk about the database. I use Prisma with PostgreSQL for its type safety and migrations. Here’s a snippet from my Prisma schema defining a User model:

model User {
  id        String   @id @default(cuid())
  email     String   @unique
  name      String?
  posts     Post[]
  createdAt DateTime @default(now())
}

Connecting Prisma to Fastify is simple with plugins. I decorate the Fastify instance to make the Prisma client available everywhere. This way, I can access the database directly from my resolvers without extra boilerplate.

But what about performance? GraphQL can suffer from the N+1 query problem, where multiple database calls slow things down. That’s where data loaders come in. They batch and cache requests to the database. For example, when fetching posts by multiple users, a data loader ensures we make only one query per user instead of one per post.

Caching is where Redis shines. I use it to store frequently accessed data, reducing database load. Here’s how I set up a basic cache service:

async get<T>(prefix: string, id: string): Promise<T | null> {
  const key = `${prefix}:${id}`;
  const cached = await this.server.redis.get(key);
  return cached ? JSON.parse(cached) : null;
}

This method checks if data is in Redis before hitting the database. How much faster could your API be with strategic caching?

Error handling and logging are often overlooked. I integrate Pino for logging because it’s fast and integrates well with Fastify. For errors, I create custom GraphQL errors that provide clear messages without exposing sensitive information. Have you considered how error clarity affects developer experience?

Subscriptions add real-time capabilities. With Mercurius, setting up GraphQL subscriptions for live updates is straightforward. I use them for features like live notifications or chat messages, ensuring users get instant feedback.

Deployment and monitoring are the final steps. I use tools like Autocannon for load testing and ensure my API is production-ready with health checks and metrics. Monitoring helps me catch issues before users do.

Building a high-performance GraphQL API isn’t just about writing code; it’s about making smart architectural choices. By combining Fastify’s speed, Mercurius’s efficiency, and Redis’s caching, you can create APIs that handle heavy loads gracefully.

If this guide helped you understand how to optimize your GraphQL setup, please like and share it with your team. I’d love to hear about your experiences—drop a comment below with your thoughts or questions!